{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#本章需导入的模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif']=['SimHei']  #解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "import sklearn.linear_model as LM\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_validate,train_test_split\n",
    "from sklearn import neighbors,preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>剧名</th>\n",
       "      <th>类型</th>\n",
       "      <th>播放量</th>\n",
       "      <th>点赞</th>\n",
       "      <th>差评</th>\n",
       "      <th>得分</th>\n",
       "      <th>采集日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>花千骨2015</td>\n",
       "      <td>言情剧\\n/\\n穿越剧\\n/\\n网络剧</td>\n",
       "      <td>3.07亿</td>\n",
       "      <td>992342</td>\n",
       "      <td>357808.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015-9-23 23:48:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>还珠格格2015</td>\n",
       "      <td>古装剧\\n\\n/\\n喜剧\\n\\n/\\n网络剧</td>\n",
       "      <td>73.3万</td>\n",
       "      <td>2352</td>\n",
       "      <td>7240.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2015-9-23 23:48:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>天局</td>\n",
       "      <td>武侠剧\\n/\\n古装剧\\n/\\n悬疑剧\\n/\\n网络剧</td>\n",
       "      <td>3454万</td>\n",
       "      <td>38746</td>\n",
       "      <td>3593.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2015-9-23 23:48:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>明若晓溪</td>\n",
       "      <td>青春剧\\n/\\n言情剧\\n/\\n偶像剧</td>\n",
       "      <td>1.57亿</td>\n",
       "      <td>518660</td>\n",
       "      <td>72508.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2015-9-23 23:48:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>多情江山</td>\n",
       "      <td>言情剧\\n/\\n古装剧\\n/\\n宫廷剧</td>\n",
       "      <td>1126万</td>\n",
       "      <td>22553</td>\n",
       "      <td>6955.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2015-9-23 23:48:52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         剧名                           类型    播放量      点赞        差评   得分  \\\n",
       "0   花千骨2015          言情剧\\n/\\n穿越剧\\n/\\n网络剧  3.07亿  992342  357808.0  7.3   \n",
       "1  还珠格格2015       古装剧\\n\\n/\\n喜剧\\n\\n/\\n网络剧  73.3万    2352    7240.0  2.5   \n",
       "2        天局  武侠剧\\n/\\n古装剧\\n/\\n悬疑剧\\n/\\n网络剧  3454万   38746    3593.0  9.2   \n",
       "3      明若晓溪          青春剧\\n/\\n言情剧\\n/\\n偶像剧  1.57亿  518660   72508.0  8.8   \n",
       "4      多情江山          言情剧\\n/\\n古装剧\\n/\\n宫廷剧  1126万   22553    6955.0  7.6   \n",
       "\n",
       "                 采集日期  \n",
       "0  2015-9-23 23:48:48  \n",
       "1  2015-9-23 23:48:48  \n",
       "2  2015-9-23 23:48:52  \n",
       "3  2015-9-23 23:48:51  \n",
       "4  2015-9-23 23:48:52  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('电视剧播放数据.xlsx')\n",
    "data=data.replace(0,np.NaN)\n",
    "data=data.dropna()\n",
    "data=data.loc[(data['点赞']<=2000000) & (data['差评']<=2000000)]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-近邻：测试精度=0.969754总预测精度=0.971691\n",
      "一般线性回归模型：测试精度=0.179853;总预测精度=0.176142\n"
     ]
    }
   ],
   "source": [
    "X=data.loc[:,['点赞','差评']]\n",
    "Y=data.loc[:,'得分']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,train_size=0.70, random_state=123) \n",
    "modelKNN=neighbors.KNeighborsRegressor(n_neighbors=20)\n",
    "modelKNN.fit(X_train,Y_train)\n",
    "print('K-近邻：测试精度=%f总预测精度=%f'%(modelKNN.score(X_test,Y_test),modelKNN.score(X,Y)))\n",
    "modelLR=LM.LinearRegression()\n",
    "modelLR.fit(X_train,Y_train)\n",
    "print('一般线性回归模型：测试精度=%f;总预测精度=%f'%(modelLR.score(X_test,Y_test),modelLR.score(X,Y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19ae2afebc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(12,4))\n",
    "axes[0].scatter(X.iloc[:,0],Y,s=2,c='r')\n",
    "index = np.argsort(X.iloc[:,0])\n",
    "axes[0].plot(X.iloc[index,0],modelKNN.predict(X.iloc[index,:]),linewidth=0.5,label=\"K-近邻\")\n",
    "axes[0].plot(X.iloc[index,0],modelLR.predict(X.iloc[index,:]),linestyle='--',linewidth=1,label=\"一般线性回归模型\")\n",
    "axes[0].set_title('点赞数和得分',fontsize=12)\n",
    "axes[0].set_xlabel('点赞数',fontsize=12)\n",
    "axes[0].set_ylabel('得分',fontsize=12)\n",
    "axes[0].legend()\n",
    "\n",
    "data=data.loc[(data['点赞']<=250000)]\n",
    "axes[1].scatter(data['点赞'],data['得分'],s=2,c='r')\n",
    "T=X.loc[(X['点赞']<=250000)]\n",
    "index = np.argsort(T.iloc[:,0])\n",
    "axes[1].plot(T.iloc[index,0],modelKNN.predict(T.iloc[index,:]),linewidth=0.5,label=\"K-近邻\")\n",
    "axes[1].plot(T.iloc[index,0],modelLR.predict(T.iloc[index,:]),linestyle='--',linewidth=1,label=\"一般线性回归模型\")\n",
    "axes[1].set_title('点赞数和得分',fontsize=12)\n",
    "axes[1].set_xlabel('点赞数',fontsize=12)\n",
    "axes[1].set_ylabel('得分',fontsize=12)\n",
    "axes[1].legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
